Overview

Dataset statistics

Number of variables36
Number of observations57694
Missing cells0
Missing cells (%)0.0%
Duplicate rows18308
Duplicate rows (%)31.7%
Total size in memory9.7 MiB
Average record size in memory176.0 B

Variable types

BOOL17
NUM16
CAT3

Reproduction

Analysis started2021-04-21 05:41:50.022694
Analysis finished2021-04-21 05:42:44.381787
Duration54.36 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

year has constant value "2019" Constant
Dataset has 18308 (31.7%) duplicate rows Duplicates
bytes_sent is highly correlated with bytes and 2 other fieldsHigh correlation
bytes is highly correlated with bytes_sent and 3 other fieldsHigh correlation
packets is highly correlated with bytes and 3 other fieldsHigh correlation
bytes_received is highly correlated with packets and 1 other fieldsHigh correlation
ts_ret_a is highly correlated with timestamp and 1 other fieldsHigh correlation
timestamp is highly correlated with ts_ret_a and 1 other fieldsHigh correlation
timestamp_relative is highly correlated with timestamp and 1 other fieldsHigh correlation
packets_sent is highly correlated with bytes and 2 other fieldsHigh correlation
packets_received is highly correlated with bytes_received and 1 other fieldsHigh correlation
dayofweek is highly correlated with dayHigh correlation
day is highly correlated with dayofweekHigh correlation
avg_bytes_per_packet is highly correlated with avg_bytes_received_per_packetHigh correlation
avg_bytes_received_per_packet is highly correlated with avg_bytes_per_packetHigh correlation
packets_per_rel_time is highly correlated with bytes and 1 other fieldsHigh correlation
source_port_cat_Dynamic and/or Private Ports is highly correlated with source_port_cat_Registered PortsHigh correlation
source_port_cat_Registered Ports is highly correlated with source_port_cat_Dynamic and/or Private PortsHigh correlation
nat_source_port_cat_Well Known Ports is highly correlated with action_allowHigh correlation
action_allow is highly correlated with nat_source_port_cat_Well Known PortsHigh correlation
nat_dest_port_cat_Registered Ports is highly correlated with nat_dest_port_cat_Well Known PortsHigh correlation
nat_dest_port_cat_Well Known Ports is highly correlated with nat_dest_port_cat_Registered PortsHigh correlation
dayofweek is highly correlated with dayHigh correlation
day is highly correlated with dayofweekHigh correlation
bytes is highly skewed (γ1 = 177.9774935) Skewed
bytes_sent is highly skewed (γ1 = 208.2272139) Skewed
bytes_received is highly skewed (γ1 = 98.96945013) Skewed
packets is highly skewed (γ1 = 138.2226542) Skewed
packets_sent is highly skewed (γ1 = 170.4626862) Skewed
packets_received is highly skewed (γ1 = 112.9164773) Skewed
packets_transfer_fraction is highly skewed (γ1 = 210.2490592) Skewed
packets_per_rel_time is highly skewed (γ1 = 230.2782051) Skewed
bytes_received has 28008 (48.5%) zeros Zeros
time_elapsed_sec has 25027 (43.4%) zeros Zeros
packets_received has 28008 (48.5%) zeros Zeros
hour has 4223 (7.3%) zeros Zeros
avg_bytes_received_per_packet has 28008 (48.5%) zeros Zeros
packets_transfer_fraction has 28008 (48.5%) zeros Zeros

Variables

bytes
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct count8933
Unique (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98256.55179048081
Minimum60
Maximum1269359015
Zeros0
Zeros (%)0.0%
Memory size450.7 KiB
2021-04-21T08:42:44.440422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile62
Q166
median168
Q3734.75
95-th percentile19962.8
Maximum1269359015
Range1269358955
Interquartile range (IQR)668.75

Descriptive statistics

Standard deviation5992140.403
Coefficient of variation (CV)60.98463963
Kurtosis35813.12688
Mean98256.55179
Median Absolute Deviation (MAD)102
Skewness177.9774935
Sum5668813499
Variance3.590574661e+13
2021-04-21T08:42:44.512003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
70960316.6%
 
66840214.6%
 
6248458.4%
 
6015062.6%
 
1469271.6%
 
1777391.3%
 
1686391.1%
 
1996211.1%
 
1845430.9%
 
1833990.7%
 
Other values (8923)2947051.1%
 
ValueCountFrequency (%) 
6015062.6%
 
6248458.4%
 
645< 0.1%
 
66840214.6%
 
684< 0.1%
 
ValueCountFrequency (%) 
12693590151< 0.1%
 
4289359142< 0.1%
 
1276535071< 0.1%
 
1264905141< 0.1%
 
781989702< 0.1%
 

bytes_sent
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct count5794
Unique (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28535.367334558185
Minimum60
Maximum948477220
Zeros0
Zeros (%)0.0%
Memory size450.7 KiB
2021-04-21T08:42:44.594003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile62
Q166
median88
Q3192
95-th percentile4684
Maximum948477220
Range948477160
Interquartile range (IQR)126

Descriptive statistics

Standard deviation4175410.861
Coefficient of variation (CV)146.3240621
Kurtosis46397.45895
Mean28535.36733
Median Absolute Deviation (MAD)22
Skewness208.2272139
Sum1646319483
Variance1.743405586e+13
2021-04-21T08:42:44.655002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
70992217.2%
 
66844014.6%
 
6248628.4%
 
9433455.8%
 
10229935.2%
 
6015982.8%
 
8612972.2%
 
7811241.9%
 
11010671.8%
 
1469121.6%
 
Other values (5784)2213438.4%
 
ValueCountFrequency (%) 
6015982.8%
 
6248628.4%
 
64300.1%
 
66844014.6%
 
684< 0.1%
 
ValueCountFrequency (%) 
9484772201< 0.1%
 
2134436412< 0.1%
 
1226611161< 0.1%
 
41193152< 0.1%
 
27269811< 0.1%
 

bytes_received
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct count7371
Unique (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69721.18445592263
Minimum0
Maximum320881795
Zeros28008
Zeros (%)48.5%
Memory size450.7 KiB
2021-04-21T08:42:44.733038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median74
Q3417
95-th percentile12816.55
Maximum320881795
Range320881795
Interquartile range (IQR)417

Descriptive statistics

Standard deviation2155345.268
Coefficient of variation (CV)30.91377871
Kurtosis12252.47266
Mean69721.18446
Median Absolute Deviation (MAD)74
Skewness98.96945013
Sum4022494016
Variance4.645513224e+12
2021-04-21T08:42:44.804038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02800848.5%
 
839141.6%
 
907811.4%
 
975430.9%
 
895270.9%
 
914910.9%
 
744660.8%
 
933640.6%
 
823590.6%
 
1263310.6%
 
Other values (7361)2491043.2%
 
ValueCountFrequency (%) 
02800848.5%
 
603190.6%
 
62780.1%
 
6312< 0.1%
 
64390.1%
 
ValueCountFrequency (%) 
3208817951< 0.1%
 
2154922732< 0.1%
 
1237635331< 0.1%
 
761573182< 0.1%
 
581229022< 0.1%
 

packets
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct count946
Unique (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.56787534232329
Minimum1
Maximum1036116
Zeros0
Zeros (%)0.0%
Memory size450.7 KiB
2021-04-21T08:42:44.884661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile49
Maximum1036116
Range1036115
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5900.373747
Coefficient of variation (CV)53.36426814
Kurtosis21169.63233
Mean110.5678753
Median Absolute Deviation (MAD)1
Skewness138.2226542
Sum6379103
Variance34814410.36
2021-04-21T08:42:44.952312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12647645.9%
 
21460225.3%
 
416282.8%
 
69441.6%
 
106271.1%
 
196211.1%
 
205190.9%
 
185100.9%
 
165000.9%
 
174630.8%
 
Other values (936)1080418.7%
 
ValueCountFrequency (%) 
12647645.9%
 
21460225.3%
 
34320.7%
 
416282.8%
 
51280.2%
 
ValueCountFrequency (%) 
10361161< 0.1%
 
6359462< 0.1%
 
1610301< 0.1%
 
1237382< 0.1%
 
1232751< 0.1%
 

time_elapsed_sec
Real number (ℝ≥0)

ZEROS

Distinct count830
Unique (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.10826082434916
Minimum0
Maximum10824
Zeros25027
Zeros (%)43.4%
Memory size450.7 KiB
2021-04-21T08:42:45.030973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q330
95-th percentile195
Maximum10824
Range10824
Interquartile range (IQR)30

Descriptive statistics

Standard deviation309.7017402
Coefficient of variation (CV)4.614957032
Kurtosis208.1360332
Mean67.10826082
Median Absolute Deviation (MAD)15
Skewness12.18161335
Sum3871744
Variance95915.16787
2021-04-21T08:42:45.096938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02502743.4%
 
30913215.8%
 
3128865.0%
 
2918713.2%
 
1515952.8%
 
512052.1%
 
168621.5%
 
87581.3%
 
12005290.9%
 
324620.8%
 
Other values (820)1336723.2%
 
ValueCountFrequency (%) 
02502743.4%
 
1330.1%
 
25< 0.1%
 
31< 0.1%
 
43090.5%
 
ValueCountFrequency (%) 
108241< 0.1%
 
92831< 0.1%
 
91151< 0.1%
 
89121< 0.1%
 
81691< 0.1%
 

timestamp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count32806
Unique (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1552632004606.116
Minimum1552573800000
Maximum1552695187200
Zeros0
Zeros (%)0.0%
Memory size450.7 KiB
2021-04-21T08:42:45.180973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.5525738e+12
5-th percentile1.552578755e+12
Q11.552601348e+12
median1.552629462e+12
Q31.552663132e+12
95-th percentile1.552688834e+12
Maximum1.552695187e+12
Range121387200
Interquartile range (IQR)61784064

Descriptive statistics

Standard deviation35980075.78
Coefficient of variation (CV)2.317360177e-05
Kurtosis-1.257592663
Mean1.552632005e+12
Median Absolute Deviation (MAD)31407336
Skewness0.09821640971
Sum8.957755087e+16
Variance1.294565853e+15
2021-04-21T08:42:45.254974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.552583288e+124060.7%
 
1.552583583e+123280.6%
 
1.552580488e+121610.3%
 
1.552606678e+121510.3%
 
1.552577633e+121490.3%
 
1.552600289e+121460.3%
 
1.552583075e+121450.3%
 
1.552639718e+121440.2%
 
1.552578688e+121430.2%
 
1.55260559e+121420.2%
 
Other values (32796)5577996.7%
 
ValueCountFrequency (%) 
1.5525738e+121< 0.1%
 
1.552573801e+121< 0.1%
 
1.55257383e+121< 0.1%
 
1.552573831e+121< 0.1%
 
1.552573831e+121< 0.1%
 
ValueCountFrequency (%) 
1.552695187e+121< 0.1%
 
1.552695186e+121< 0.1%
 
1.552695185e+121< 0.1%
 
1.552695185e+121< 0.1%
 
1.552695181e+1221< 0.1%
 

ts_ret_a
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count29239
Unique (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54370635.3100149
Minimum0
Maximum103540944
Zeros1
Zeros (%)< 0.1%
Memory size450.7 KiB
2021-04-21T08:42:45.348599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4955148
Q127548232
median55662120
Q383831166
95-th percentile95950008
Maximum103540944
Range103540944
Interquartile range (IQR)56282934

Descriptive statistics

Standard deviation31030807.56
Coefficient of variation (CV)0.5707273307
Kurtosis-1.3557186
Mean54370635.31
Median Absolute Deviation (MAD)28113888
Skewness-0.1629404262
Sum3.136859434e+12
Variance9.629110177e+14
2021-04-21T08:42:45.415244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
94880404060.7%
 
97827363280.6%
 
888529682790.5%
 
921610562700.5%
 
830333282640.5%
 
807324962590.4%
 
954526562490.4%
 
800816642480.4%
 
835889042450.4%
 
846635042370.4%
 
Other values (29229)5490995.2%
 
ValueCountFrequency (%) 
01< 0.1%
 
11281< 0.1%
 
303121< 0.1%
 
306961< 0.1%
 
310801< 0.1%
 
ValueCountFrequency (%) 
1035409443< 0.1%
 
1035390961< 0.1%
 
1035387361< 0.1%
 
1035385441< 0.1%
 
1035378249< 0.1%
 

timestamp_relative
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count29239
Unique (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2265443.1379172876
Minimum0
Maximum4314206
Zeros1
Zeros (%)< 0.1%
Memory size450.7 KiB
2021-04-21T08:42:45.496800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile206464.5
Q11147843
median2319255
Q33492965.25
95-th percentile3997917
Maximum4314206
Range4314206
Interquartile range (IQR)2345122.25

Descriptive statistics

Standard deviation1292950.315
Coefficient of variation (CV)0.5707273307
Kurtosis-1.3557186
Mean2265443.138
Median Absolute Deviation (MAD)1171412
Skewness-0.1629404262
Sum1.307024764e+11
Variance1.671720517e+12
2021-04-21T08:42:45.559801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3953354060.7%
 
4076143280.6%
 
37022072790.5%
 
38400442700.5%
 
34597222640.5%
 
33638542590.4%
 
39771942490.4%
 
33367362480.4%
 
34828712450.4%
 
35276462370.4%
 
Other values (29229)5490995.2%
 
ValueCountFrequency (%) 
01< 0.1%
 
471< 0.1%
 
12631< 0.1%
 
12791< 0.1%
 
12951< 0.1%
 
ValueCountFrequency (%) 
43142063< 0.1%
 
43141291< 0.1%
 
43141141< 0.1%
 
43141061< 0.1%
 
43140769< 0.1%
 

packets_sent
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct count631
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.83651679550733
Minimum1
Maximum747520
Zeros0
Zeros (%)0.0%
Memory size450.7 KiB
2021-04-21T08:42:45.637800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile24
Maximum747520
Range747519
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3647.842009
Coefficient of variation (CV)76.25643031
Kurtosis32341.03893
Mean47.8365168
Median Absolute Deviation (MAD)0
Skewness170.4626862
Sum2759880
Variance13306751.33
2021-04-21T08:42:45.705801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
14004969.4%
 
224644.3%
 
313222.3%
 
1110821.9%
 
610401.8%
 
88691.5%
 
58681.5%
 
108481.5%
 
98451.5%
 
78391.5%
 
Other values (621)746812.9%
 
ValueCountFrequency (%) 
14004969.4%
 
224644.3%
 
313222.3%
 
46811.2%
 
58681.5%
 
ValueCountFrequency (%) 
7475201< 0.1%
 
3087382< 0.1%
 
829071< 0.1%
 
439172< 0.1%
 
414661< 0.1%
 

packets_received
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct count792
Unique (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.28677158803342
Minimum0
Maximum327208
Zeros28008
Zeros (%)48.5%
Memory size450.7 KiB
2021-04-21T08:42:45.792801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile25
Maximum327208
Range327208
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2457.860601
Coefficient of variation (CV)39.46039485
Kurtosis14258.49584
Mean62.28677159
Median Absolute Deviation (MAD)1
Skewness112.9164773
Sum3593573
Variance6041078.732
2021-04-21T08:42:45.865421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02800848.5%
 
11424024.7%
 
216032.8%
 
811171.9%
 
310381.8%
 
910201.8%
 
78861.5%
 
108611.5%
 
58231.4%
 
47421.3%
 
Other values (782)735612.8%
 
ValueCountFrequency (%) 
02800848.5%
 
11424024.7%
 
216032.8%
 
310381.8%
 
47421.3%
 
ValueCountFrequency (%) 
3272082< 0.1%
 
2885961< 0.1%
 
818091< 0.1%
 
798212< 0.1%
 
781231< 0.1%
 

day
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size450.7 KiB
15
39787
14
14140
16
 
3767
ValueCountFrequency (%) 
153978769.0%
 
141414024.5%
 
1637676.5%
 
2021-04-21T08:42:46.177750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

hour
Real number (ℝ≥0)

ZEROS

Distinct count24
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.48472978125975
Minimum0
Maximum23
Zeros4223
Zeros (%)7.3%
Memory size450.7 KiB
2021-04-21T08:42:46.247785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median15
Q319
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.718892527
Coefficient of variation (CV)0.6182666875
Kurtosis-1.362438247
Mean12.48472978
Median Absolute Deviation (MAD)6
Skewness-0.304946079
Sum720294
Variance59.58130184
2021-04-21T08:42:46.308784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
042237.3%
 
1740307.0%
 
1940066.9%
 
135956.2%
 
2135406.1%
 
2334496.0%
 
1833015.7%
 
2232295.6%
 
2029245.1%
 
1628344.9%
 
Other values (14)2256339.1%
 
ValueCountFrequency (%) 
042237.3%
 
135956.2%
 
221733.8%
 
317943.1%
 
418683.2%
 
ValueCountFrequency (%) 
2334496.0%
 
2232295.6%
 
2135406.1%
 
2029245.1%
 
1940066.9%
 

year
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size450.7 KiB
2019
57694
ValueCountFrequency (%) 
201957694100.0%
 
2021-04-21T08:42:46.589143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

dayofweek
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size450.7 KiB
4
39787
3
14140
5
 
3767
ValueCountFrequency (%) 
43978769.0%
 
31414024.5%
 
537676.5%
 
2021-04-21T08:42:46.865788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

avg_bytes_received_per_packet
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count8403
Unique (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.28404356197723
Minimum0.0
Maximum1513.9154804270463
Zeros28008
Zeros (%)48.5%
Memory size450.7 KiB
2021-04-21T08:42:46.941427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median61.5
Q3146
95-th percentile769
Maximum1513.91548
Range1513.91548
Interquartile range (IQR)146

Descriptive statistics

Standard deviation297.481276
Coefficient of variation (CV)1.778300367
Kurtosis6.169713093
Mean167.2840436
Median Absolute Deviation (MAD)61.5
Skewness2.489507765
Sum9651285.609
Variance88495.10957
2021-04-21T08:42:47.006445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02800848.5%
 
839421.6%
 
908571.5%
 
606401.1%
 
975801.0%
 
895470.9%
 
915300.9%
 
745210.9%
 
823850.7%
 
933770.7%
 
Other values (8393)2430742.1%
 
ValueCountFrequency (%) 
02800848.5%
 
606401.1%
 
60.461538462< 0.1%
 
60.51< 0.1%
 
60.545454551< 0.1%
 
ValueCountFrequency (%) 
1513.915481< 0.1%
 
1513.4722471< 0.1%
 
1513.4504711< 0.1%
 
1513.3890541< 0.1%
 
1513.1355671< 0.1%
 

avg_bytes_sent_per_packet
Real number (ℝ≥0)

Distinct count8033
Unique (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.82129485497478
Minimum30.0
Maximum1514.0
Zeros0
Zeros (%)0.0%
Memory size450.7 KiB
2021-04-21T08:42:47.084141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile50.5
Q166
median74
Q3102.8869904
95-th percentile234
Maximum1514
Range1484
Interquartile range (IQR)36.88699041

Descriptive statistics

Standard deviation88.56841522
Coefficient of variation (CV)0.8530852495
Kurtosis55.62895297
Mean103.8212949
Median Absolute Deviation (MAD)14
Skewness5.998635781
Sum5989865.785
Variance7844.364175
2021-04-21T08:42:47.152140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
70899115.6%
 
66832114.4%
 
6248788.5%
 
9433005.7%
 
10229605.1%
 
6015322.7%
 
8612902.2%
 
7811402.0%
 
11010421.8%
 
359451.6%
 
Other values (8023)2329540.4%
 
ValueCountFrequency (%) 
30780.1%
 
3215< 0.1%
 
331390.2%
 
359451.6%
 
35.51< 0.1%
 
ValueCountFrequency (%) 
15144< 0.1%
 
14843< 0.1%
 
1479.5025271< 0.1%
 
14681< 0.1%
 
14461< 0.1%
 

avg_bytes_per_packet
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count10337
Unique (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.64381284697097
Minimum8.393298245614035
Maximum1514.0
Zeros0
Zeros (%)0.0%
Memory size450.7 KiB
2021-04-21T08:42:47.237105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum8.393298246
5-th percentile60
Q166
median83
Q3146
95-th percentile505.9038462
Maximum1514
Range1505.606702
Interquartile range (IQR)80

Descriptive statistics

Standard deviation181.8829688
Coefficient of variation (CV)1.146486368
Kurtosis9.671593626
Mean158.6438128
Median Absolute Deviation (MAD)21
Skewness2.920567208
Sum9152796.138
Variance33081.41433
2021-04-21T08:42:47.304109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
70879415.2%
 
66829014.4%
 
6248748.4%
 
6015112.6%
 
1469241.6%
 
358311.4%
 
88.57411.3%
 
846461.1%
 
99.56141.1%
 
925400.9%
 
Other values (10327)2992951.9%
 
ValueCountFrequency (%) 
8.3932982461< 0.1%
 
302< 0.1%
 
331310.2%
 
358311.4%
 
38.51< 0.1%
 
ValueCountFrequency (%) 
15144< 0.1%
 
14843< 0.1%
 
14681< 0.1%
 
1347.4470441< 0.1%
 
13451< 0.1%
 

packets_transfer_fraction
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count1920
Unique (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5847293159254474
Minimum0.0
Maximum863.6666666666666
Zeros28008
Zeros (%)48.5%
Memory size450.7 KiB
2021-04-21T08:42:47.385726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5172413793
Q31
95-th percentile1.4
Maximum863.6666667
Range863.6666667
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.767052497
Coefficient of variation (CV)6.44238692
Kurtosis47817.44462
Mean0.5847293159
Median Absolute Deviation (MAD)0.5172413793
Skewness210.2490592
Sum33735.37315
Variance14.19068451
2021-04-21T08:42:47.449347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02800848.5%
 
11691429.3%
 
1.55991.0%
 
25931.0%
 
1.255040.9%
 
1.3333333334580.8%
 
34500.8%
 
1.24350.8%
 
1.3753310.6%
 
1.2222222223060.5%
 
Other values (1910)909615.8%
 
ValueCountFrequency (%) 
02800848.5%
 
0.0057077625571< 0.1%
 
0.0084151472651< 0.1%
 
0.014044943821< 0.1%
 
0.031746031751< 0.1%
 
ValueCountFrequency (%) 
863.66666671< 0.1%
 
139.83333331< 0.1%
 
100.78571432< 0.1%
 
72.641025641< 0.1%
 
58.1252< 0.1%
 

packets_per_rel_time
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct count29459
Unique (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7025604860322e-05
Minimum0.0
Maximum1.3789083396358672
Zeros1
Zeros (%)< 0.1%
Memory size450.7 KiB
2021-04-21T08:42:47.529074image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.547790174e-07
Q13.161913612e-07
median7.548559887e-07
Q33.051573293e-06
95-th percentile2.672168026e-05
Maximum1.37890834
Range1.37890834
Interquartile range (IQR)2.735381931e-06

Descriptive statistics

Standard deviation0.005825808768
Coefficient of variation (CV)102.1612797
Kurtosis54402.44306
Mean5.702560486e-05
Median Absolute Deviation (MAD)4.860390012e-07
Skewness230.2782051
Sum3.290035247
Variance3.39400478e-05
2021-04-21T08:42:47.597075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.529500297e-064060.7%
 
2.453301408e-063270.6%
 
2.604136828e-072700.5%
 
2.972780626e-072580.4%
 
2.890405645e-072560.4%
 
2.514335484e-072490.4%
 
2.996940723e-072480.4%
 
2.871194483e-072430.4%
 
2.701091538e-072390.4%
 
2.912111881e-072330.4%
 
Other values (29449)5496595.3%
 
ValueCountFrequency (%) 
01< 0.1%
 
2.317923623e-073< 0.1%
 
2.317993471e-079< 0.1%
 
2.318010127e-071< 0.1%
 
2.318099325e-071< 0.1%
 
ValueCountFrequency (%) 
1.378908341< 0.1%
 
0.14784023131< 0.1%
 
0.085442654721< 0.1%
 
0.077590121762< 0.1%
 
0.066684568561< 0.1%
 

action_allow
Boolean

HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
1
32969
0
24725
ValueCountFrequency (%) 
13296957.1%
 
02472542.9%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
44634
1
13060
ValueCountFrequency (%) 
04463477.4%
 
11306022.6%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
46074
1
11620
ValueCountFrequency (%) 
04607479.9%
 
11162020.1%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
57649
1
 
45
ValueCountFrequency (%) 
05764999.9%
 
1450.1%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size450.7 KiB
0
44281
1
13413
ValueCountFrequency (%) 
04428176.8%
 
11341323.2%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
57261
1
 
433
ValueCountFrequency (%) 
05726199.2%
 
14330.8%
 

source_port_cat_Registered Ports
Boolean

HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
43869
1
13825
ValueCountFrequency (%) 
04386976.0%
 
11382524.0%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
1
43436
0
14258
ValueCountFrequency (%) 
14343675.3%
 
01425824.7%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
1
39814
0
17880
ValueCountFrequency (%) 
13981469.0%
 
01788031.0%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
44790
1
12904
ValueCountFrequency (%) 
04479077.6%
 
11290422.4%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
52718
1
 
4976
ValueCountFrequency (%) 
05271891.4%
 
149768.6%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
32520
1
25174
ValueCountFrequency (%) 
03252056.4%
 
12517443.6%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
34056
1
23638
ValueCountFrequency (%) 
03405659.0%
 
12363841.0%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
48812
1
 
8882
ValueCountFrequency (%) 
04881284.6%
 
1888215.4%
 

nat_dest_port_cat_Well Known Ports
Boolean

HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
1
52302
0
 
5392
ValueCountFrequency (%) 
15230290.7%
 
053929.3%
 

nat_dest_port_cat_Registered Ports
Boolean

HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
53183
1
 
4511
ValueCountFrequency (%) 
05318392.2%
 
145117.8%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
0
56813
1
 
881
ValueCountFrequency (%) 
05681398.5%
 
18811.5%
 

Interactions

2021-04-21T08:42:07.171891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:07.304523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:07.433675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:07.566646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:07.688678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:07.818839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:07.951185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:08.076293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:08.196294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:08.325998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:08.454788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:08.578825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:08.716821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:08.850721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:08.975333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:09.091367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:09.217334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:09.330034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:09.439370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:09.560336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:09.679335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:09.803334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:09.929215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:10.050216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:11.224752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:11.342126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:11.464762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:11.574767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:11.699728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:11.833378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:11.954240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:12.064228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:12.182227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:12.300235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:12.418049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:12.545664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:12.679631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:12.814631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:12.950540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:13.073584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:13.194540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:13.325565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:13.454485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:13.576484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:13.711484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:13.856073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:13.987336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:14.117314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:14.256315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:14.387540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:14.514102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:14.657099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:14.780232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:14.904044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:15.033684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:15.180692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:15.307720image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:15.440944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:15.574539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:15.684541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:15.807837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:15.953052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:16.071623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:16.320345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:16.460656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:16.599185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:16.722507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:16.859054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:16.996393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:17.124460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:17.249496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:17.371075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:17.493732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:17.627438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:17.759436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:17.880064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:18.010792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:18.141485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:18.266484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:18.382050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:18.510738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:18.645328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:18.772369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:18.906948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:19.032564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:19.226214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:19.413789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:19.559180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:19.700145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:19.842353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:19.981976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-04-21T08:42:20.866241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-04-21T08:42:21.892526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-04-21T08:42:22.169785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:22.286786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:22.423373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:22.566945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:22.706579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:23.014847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-04-21T08:42:23.453084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:23.586764image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:23.714433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:23.841042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:23.968085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:24.095690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-04-21T08:42:24.336500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:24.462465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:24.580086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:24.713673image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:24.845273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:24.974273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:25.098854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:25.230468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:25.362071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:25.484104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:25.614715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:25.739331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:25.870905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:26.009905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:26.145494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:26.287148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:26.420689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:26.556690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:26.682921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:26.823921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:26.960541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:27.097252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:27.220373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:27.355086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:27.479038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:27.603619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:27.735231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:27.864289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:27.997289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:28.137895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:28.270526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:28.390126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:28.523093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:28.658162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:28.775814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:28.906348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:29.042347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:29.175469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:29.299080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:29.435654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:29.553653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:29.664314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:29.774983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:29.889140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:30.006104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:30.127105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:30.245306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:30.361909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:30.490908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:30.808199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:30.911836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:31.037834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:31.155799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:31.275058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:31.387650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:31.508690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:31.642685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:31.765430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:31.899043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:32.031056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:32.169043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:32.307312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:32.439960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:32.566965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:32.701617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:32.841867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:32.967867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:33.107866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:33.247478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:33.385710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:33.509754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:33.648790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:33.782423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:33.903771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:34.030816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:34.159806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:34.293442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:34.426650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:34.557651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:34.684651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:34.817985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:34.954602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:35.077601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:35.214608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:35.352677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:35.483182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:35.618412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:35.797806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:35.936525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:36.063077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:36.202177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:36.329520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:36.460714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:36.593761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:36.728754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:36.857635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:36.997633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:37.127636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:37.242599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:37.378477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:37.508478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:37.638522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:37.764478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:37.899317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:38.017309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:38.129319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:38.251312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:38.363243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:38.484208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:38.600250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:38.715207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:38.836336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:38.965958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:39.086922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:39.197922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:39.319665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:39.445887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:39.561887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:39.671928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:39.795924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:39.927784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:40.296825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:40.421083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:40.542083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:40.667082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:40.796165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:40.928035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:41.054036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:41.188000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:41.315998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:41.435467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:41.564431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:41.694431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:41.824471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:41.949679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2021-04-21T08:42:47.753042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-21T08:42:48.294000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-21T08:42:48.828369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-21T08:42:49.375819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-21T08:42:49.819034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-21T08:42:42.325679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-04-21T08:42:43.856939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

bytesbytes_sentbytes_receivedpacketstime_elapsed_sectimestampts_ret_atimestamp_relativepackets_sentpackets_receiveddayhouryeardayofweekavg_bytes_received_per_packetavg_bytes_sent_per_packetavg_bytes_per_packetpackets_transfer_fractionpackets_per_rel_timeaction_allowaction_denyaction_dropaction_reset-bothis_dest_port_53source_port_cat_Well Known Portssource_port_cat_Registered Portssource_port_cat_Dynamic and/or Private Portsdest_port_cat_Well Known Portsdest_port_cat_Registered Portsdest_port_cat_Dynamic and/or Private Portsnat_source_port_cat_Well Known Portsnat_source_port_cat_Registered Portsnat_source_port_cat_Dynamic and/or Private Portsnat_dest_port_cat_Well Known Portsnat_dest_port_cat_Registered Portsnat_dest_port_cat_Dynamic and/or Private Ports
0284961884311552591724448179244487468522214212019394.000048.00000071.0000001.002.677907e-0610001001100010100
17070010155259178536017985360749390101421201930.000070.00000070.0000000.001.334419e-0600100001100100100
2173761687156892840155267556736810176736842403071216152020194980.5625140.583333620.5714290.752.829984e-0610000001100010100
366660101552655063160812631603385965101515201940.000066.00000066.0000000.002.953368e-0700100001100100100
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56060010155259120312017403120725130101421201930.000060.00000060.0000000.001.379063e-0601000010010100100
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Duplicate rows

Most frequent

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